Systems | Development | Analytics | API | Testing

At Countly, we’re passionate about empowering businesses and developers with analytics that work everywhere - even when the internet doesn’t. In a world where applications and devices don’t always stay connected, we’ve built robust capabilities to track user behavior and performance, no matter the scenario. From IoT gadgets in remote locations to industrial systems in secure facilities, we ensure you never miss a data point.

In today’s data-driven environment, there is truly no end to the overwhelming amount of information that both contributors and users have access to. People often spend hours combing through logs trying to piece information into usable goods. This turns what could be swift, data-driven decisions, into time-consuming challenges that slow down innovation.

We understand new concepts by linking them to familiar ones. These analogies aren’t just helpful; they’re how we think. For me, that something familiar is chess, and I’ll use it to explain some of the core ideas behind stream processing—a concept that requires a shift from seeing tables as static snapshots to treating tables as materialized projections of a continuous stream of changes.

In-app chat has gone from a nice-to-have to an essential product feature across gaming, SaaS, social, and live streaming apps. While it’s tempting to treat it as “just another feature,” the reality is that building chat touches nearly every layer of your stack - from low-latency delivery and message ordering to presence, typing indicators, moderation. And then there is operating at scale to consider.

Did you know that over 75% of cybersecurity incidents now involve APIs? Protecting data in transit is no longer optional - it’s a must. Here’s how encryption safeguards your API communications and why it’s critical for your business.

In insurance, the claims process is the real “moment of truth.” It's when customers find out if their insurer is truly there for them. They don’t just want fair treatment—they also expect their claims to be handled quickly and easily. But the reality? Claims often take way too long because of outdated, clunky processes. And the growing tsunami of data needed to adjust a claim can create information overload for an adjuster.

When developing software applications, data storage is a key concern. The reality is that your first concern should be the data model you choose, which in turn affects how you store data. Generally speaking, this means deciding between SQL and NoSQL databases. In this article, you will learn how to use MongoDB, a popular NoSQL database, in a Flask application. First, you will learn why MongoDB is a good choice, and then we will implement a practical hands-on project using MongoDB in Flask.

As there are many questions to answer, we have split this blog in two parts. In part 1 of this blog, we explored how Qlik is using data and insight to drive decisions and actions. In this second part, we will focus on how to overcome the challenges along the way and set up for success for the future.

Testing has evolved far beyond scattered spreadsheets and disconnected tools. Yet even with modern platforms in place, teams still run into bottlenecks, especially when fundamental tasks like test case creation are handled manually. It involves combing through acceptance criteria, writing out each step, and reviewing everything for gaps. Repeating that across multiple user stories quickly drains time and slows progress – it’s repetitive, time-intensive, and prone to inconsistency.

This article originally appeared on DevPro Journal. We’re sharing it here for our audience who may have missed it. For years, APM tools were everyone’s go-to solution for understanding how software behaved in production. And for a time, they worked, because architecture was simpler. Developers owned the backend, the frontend, and the data layer. Everything lived inside a monolith. If something went wrong, they could trace it through their codebase and fix it.